Linganisha mbinu

	Mti wa Uamuzi	FP-Growth (Frequent Pattern Growth)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1984	2000
Mwanzilishi	Breiman, Friedman, Olshen & Stone	Jiawei Han, Jian Pei & Yiwen Yin
Aina	Recursive partitioning (if-then rules)	Frequent-itemset mining algorithm
Chanzo asilia	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Majina mbadala	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Zinazohusiana	5	4
Muhtasari	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.